When functional data are not homogenous, for example, when there are multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this article, we propose a new estimation procedure for ...

Associations of land cover/land use variables and the presence of dogs in urban vs. rural address locations were evaluated retrospectively as potential risk factors for canine leptospirosis in Kansas and Nebraska using ...

Although multiracial individuals are the fastest growing population in the United States, research on the identity development of multiracial adolescents remains scant. This study explores the relationship between ethnic ...

Identification of splice sites plays a key role in annotation of genes and hence, the improvement of computational prediction of splice sites with high accuracy has great significance. In this article, we first quantitatively ...

To design ARC-111 analogues with improved efficiency, we constructed the QSAR of 22 ARC-111 analogues with RPMI8402 tumor cells. First, the optimized support vector regression (SVR) model based on the literature descriptors ...

Background: Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish ...

For linear regression models with non normally distributed errors, the least squares estimate (LSE) will lose some efficiency compared to the maximum likelihood estimate (MLE). In this article, we propose a kernel density-based ...

The problem of fitting a parametric model in Tobit errors-in-variables regression models is discussed in this paper. The proposed test is based on the supremum of the Khamaladze type transformation of a certain partial ...

A local modal estimation procedure is proposed for the regression function in a nonparametric regression model. A distinguishing characteristic of the proposed procedure
is that it introduces an additional tuning parameter ...

In this paper, we propose a new effective estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component density and the mixing ...

In this article, we study a class of semiparametric mixtures of regression models, in which the regression functions are linear functions of the predictors, but the mixing proportions are smoothing functions of a covariate.We ...

Label switching is one of the fundamental problems for Bayesian mixture model analysis.
Due to the permutation invariance of the mixture posterior, we can consider that the
posterior of a m-component mixture model is a ...

In image processing, image similarity indices evaluate how much structural information is maintained by a processed image in relation to a reference image. Commonly used measures,such as the mean squared error (MSE) and ...

Expectation-maximization (EM) algorithm has been used to maximize the likelihood function or posterior when the model contains unobserved latent variables. One main important application of EM algorithm is to find the ...

Solving label switching is crucial for interpreting the results of fitting Bayesian mixture models. The label switching originates from the invariance of posterior distribution to permutation of component labels. As a ...

In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the ...

It is well known that the normal mixture with unequal variance has unbounded likelihood and thus the corresponding global maximum likelihood estimator (MLE) is undefined. One of the commonly used solutions is to put a ...

Adverse drug events (ADEs) are a significant problem in health care. While effective warnings have the potential to reduce the prevalence of ADEs, little is known about how patients access and use prescription labeling. ...

The existing methods for tting mixture regression models assume a normal dis-
tribution for error and then estimate the regression parameters by the maximum
likelihood estimate (MLE). In this article, we demonstrate ...

A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture ...